code: def transformers_trainer(tmp_path):
      random.seed(8675309)
      np.random.seed(8675309)
      torch.manual_seed(8675309)
  
      tokenizer = DistilBertTokenizerFast.from_pretrained(
          "distilbert-base-uncased"
      )
      model = DistilBertForSequenceClassification.from_pretrained(
          "distilbert-base-uncased", num_labels=2
      )
  
      train_texts = ["I love this product!", "This is terrible."]
      train_labels = [1, 0]
  
      train_encodings = tokenizer(
          train_texts, truncation=True, padding=True
      )
  
      class CustomDataset(torch.utils.data.Dataset):
          def __init__(self, encodings, labels):
              self.encodings = encodings
              self.labels = labels
  
          def __getitem__(self, idx):
              item = {
                  key: torch.tensor(val[idx])
                  for key, val in self.encodings.items()
              }
              item["labels"] = torch.tensor(self.labels[idx])
              return item
  
          def __len__(self):
              return len(self.labels)
  
      train_dataset = CustomDataset(train_encodings, train_labels)
  
      training_args = TrainingArguments(
          output_dir=str(tmp_path.joinpath("results")),
          num_train_epochs=1,
          per_device_train_batch_size=4,
          logging_dir=str(tmp_path.joinpath("logs")),
      )
  
      return Trainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
      )
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 3 for calling a global function with example usage: transformers_trainer(tmp_path) and returns: Trainer, item, len